iAMSTEM: A Comparative Study of Active Instruction for STEM Courses

iAMSTEM: A Comparative Study of Active Instruction for STEM Courses

Called the “most important higher ed story of 2015,” the goal of the UC Davis iAMSTEM team was to improve learner outcomes in large-section introductory STEM courses and improve STEM retention rates, especially among vulnerable student populations.

Based on work by: Marco Molinaro, University of California, Davis, Chris Pagliaro, University of California, Davis

Intervention Types : Course, Software

This 2013 study of 1000 students compared outcomes for from standard lecture courses (offered to half the students) with lectures plus the use of adaptive learning tools (for the other half), along with support and training for their instructors. Students in the experimental group were 90 percent more likely than their classmates in lecture-only classes to pass the course with a “C” or better. This effect was most pronounced among the more vulnerable populations. Students’ perception of the new approach was also positive, reflecting an understanding and appreciation for the instructional techniques.

The study provided some other core lessons in understanding barriers to adopting these types of approaches. Creating incentives and motivation among the faculty is essential for these types of initiatives to succeed. Data sharing, within the institution and with collaborators, continued to be a challenge. A broader, shared understanding of data sharing norms and best practices is necessary to scale this type of work. It is also worth noting that although pre-existing open, adaptive courseware has proven successful with learners, the ability for faculty to customize this courseware is critical for adoption and long-term success.

Conclusions and Lessons Learned

  • Adaptive learning approaches can dramatically improve student learning outcomes, particularly in the most vulnerable of learners.
  • Faculty ability to customize courseware is among the factors critical for adoption and long term success.
  • Data sharing norms and practices need to be adopted and understood to scale these results

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